Objective: To establish an individualized Nomogram prediction model for predicting the postoperative recovery of patients with triad of elbow (TE) by analyzing risk factors of triad of elbow joint.
Methods: From January 2012 to December 2018, 116 patients with TE who met the criteria were collected. The independent risk factors were screened by univariate Logistic regression analysis. The statistically significant risk factors were included in the multivariate Logistic regression model. The R software was used to establish the Nomogram diagram model to predict the postoperative recovery of TE patients. C index was used to verify the discrimination, Calibration plot of the model, and the decision curve (decision curve analysis, DCA) to verify the net clinical benefit rate of the model.
Results: Forty-four of the 116 patients with TE developed symptoms after operation, with an incidence of 37.93%. Age (OR=1.930, 95% CI 1.418 to 2.764), work (OR=6.153, 95%CI 1.466 to 31.362), smoking(OR=4.463, 95%CI 1.041 to 2.291), the Mason of radial head(OR=1.348, 95%CI 2.309 to 9.348), the Regan-Morrey of coronal process (OR=4.424, 95%CI 1.751 to 2.426) and postoperative elbow immobilization time(OR=7.665, 95%CI 1.056 to 5.100) were independent risk factors for postoperative recovery of TE (P<0.05). The C-index of Nomogram plot was 0.716. Calibration plot showed that the predictive model was consistent, and the DCA curve showed satisfactory clinical net benefit.
Conclusion: The Nomogram for predicting postoperative results of TE patients based on six independent risk factors:age, work, smoking, Mason classification of radial head, Regan-Morrey classification of coronal process and immobilization time of elbow joint after operation, has good distinguishing capacity and consistency. Thepredictive model could help clinicians to identify high risk population and establish appropriate intervention strategies.
Keywords: Elbow joint; Fractures; Nomogram plot; Triad of elbow.